details_rand_forest_ordinalForest: Random forests via ordinalForest

In parsnip: A Common API to Modeling and Analysis Functions

Random forests via ordinalForest

Description

ordinalForest::ordfor() fits a model by creating a large number of regression forests using different scorings of an ordinal response, then creating a single regression forest based on an optimal subset of scorings.

Details

For this engine, there is a single mode: classification

Tuning Parameters

This model has 3 tuning parameters:

• mtry: # Randomly Selected Predictors (type: integer, default: see below)

• trees: # Trees (type: integer, default: 500L)

• min_n: Minimal Node Size (type: integer, default: see below)

mtry depends on the number of columns and the model mode. The default in ordinalForest::ordfor() is floor(sqrt(ncol(x))).

min_n depends on the mode. For regression, a value of 5 is the default. For classification, a value of 10 is used.

Translation from parsnip to the original package (classification)

rand_forest(
  mtry = integer(1),
  trees = integer(1),
  min_n = integer(1)
) |> 
  set_engine("ordinalForest") |> 
  set_mode("classification") |> 
  translate()

## Random Forest Model Specification (classification)
## 
## Main Arguments:
##   mtry = integer(1)
##   trees = integer(1)
##   min_n = integer(1)
## 
## Computational engine: ordinalForest 
## 
## Model fit template:
## ordered::ordinalForest_wrapper(x = missing_arg(), y = missing_arg(), 
##     mtry = min_cols(~integer(1), x), ntreefinal = integer(1), 
##     min.node.size = min_rows(~integer(1), x), num.threads = 1, 
##     perffunction = "probability")

Preprocessing requirements

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. ⁠{a, c}⁠ vs ⁠{b, d}⁠) when splitting at a node. Dummy variables are not required for this model.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

Other notes

By default, parallel processing is turned off. When tuning, it is more efficient to parallelize over the resamples and tuning parameters. To parallelize the construction of the trees within the model, change the num.threads argument via set_engine().

References

• Hornung R. 2020. Ordinal Forests. Journal of Classification 37:4-17. .

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on May 14, 2026, 5:08 p.m.